{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> numpy,pandas,matplotlib,这些都是一些常用的软件包,因而也需要花一些时间去掌握."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib是一个基础的画图工具,所有的都是于其类似的.\n",
    "x=np.linspace(1,10,100)\n",
    "y1,y2=np.cos(x),np.sin(x)\n",
    "plt.plot(x,y1)\n",
    "plt.plot(x,y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x213c642c550>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(x.min()*1.1,x.max()*1.1)\n",
    "plt.ylim(y1.min(),y1.max())\n",
    "plt.plot(x,y1,color='blue',linewidth=2.0,linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图形对象\n",
    "\n",
    "在matplotlib里,一个图形(figure)指的是图片的全部可视区域.可以使用plt.figure()创建,在一个图形里,可以包含多个子图(subplot),可以使用plt.subplot()创建.子图是按照网格形式排列在图形的.可以在每个子图上单独绘图."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=np.linspace(-np.pi,np.pi,200,endpoint=True)\n",
    "C,S=np.cos(X),np.sin(X)\n",
    "\n",
    "plt.figure(num='sin',figsize=(16,4))\n",
    "plt.plot(X,C)\n",
    "plt.figure(num='cos',figsize=(16,4))\n",
    "plt.plot(X,S)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn 简介"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据采集和标记\n",
    "from sklearn import datasets\n",
    "digts=datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_and_labels=list(zip(digts.images,digts.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6),dpi=300)\n",
    "for index,(image,label) in enumerate(images_and_labels[:8]):\n",
    "    plt.subplot(2,4,index+1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(image,cmap=plt.cm.gray,interpolation='nearest')\n",
    "    plt.title('Digit:%i' % label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择\n",
    "# 在进行机器学习的时候,我们需要把数据保存为样本个数*特征个数的array对象,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of raw image data:(1797, 8, 8)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of raw image data:{0}\".format(digts.images.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data:(1797, 64)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of data:{0}\".format(digts.data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据清洗预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型选择(这里使用支持向量机来识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型训练\n",
    "# 1.将数据分成训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(digts.data,digts.target,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=svm.SVC(gamma=0.001,C=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9805555555555555"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9805555555555555"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估模型的准确度\n",
    "from sklearn.metrics import accuracy_score\n",
    "Ypred = clf.predict(X_test);\n",
    "accuracy_score(y_test, Ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看预测效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize=(8, 8))\n",
    "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(X_test[i].reshape(8, 8), cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(Ypred[i]), fontsize=32,\n",
    "            transform=ax.transAxes,\n",
    "            color='green' if y_test[i] == y_test[i] else 'red')\n",
    "    ax.text(0.8, 0.05, str(y_test[i]), fontsize=32,\n",
    "            transform=ax.transAxes,\n",
    "            color='black')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型保持和加载\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['digits_svm.pkl']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clf,'digits_svm.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入模型进行预测\n",
    "clf_test=joblib.load('digits_svm.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9805555555555555"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "Ypred = clf_test.predict(X_test);\n",
    "accuracy_score(y_test, Ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn 一般性原理和通用规则\n",
    "\n",
    "sklearn包含了大部分流行的有监督学习算法和无监督学习算法.\n",
    "\n",
    "## 1.评估模型对象:\n",
    "sklearn中所有算法都已一个评估模型对象来对外提供接口,创建评估模型对象的时候,可以指定不同的参数.这个陈伟评估对象参数,评估对象参数直接影响评估模型训练时的效率以及准确性.  \n",
    "需要特别说明的是,学习机器算法原理,有一项非常重要的任务就是为了理解不同机器学习算法有哪些可以调整的参数.\n",
    "\n",
    "## 2.模型接口\n",
    "sklearn所有的评估模型都有fit()接口,这是用来u训练模型的接口,针对有监督的机器学习,使用fit(X,y)来进行训练,[y是标记数据],针对无监督学习,使用fit(X)来进行训练.  \n",
    "针对所有的有监督学习算法,sklearn的模型对象都提供了predict()接口,经过模型的训练,可以使用这个接口来进行预测.针对分类问题,有些模型还提供了predict_proba()接口,用来输出一个待预测的数据.属于各种属性的可能性.predict()接口,直接输出的是可能性最大的类别.  \n",
    "针对所有的模型,都提供了score接口,用于评价一个模型的好坏.的分与高越好."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.模型检验\n",
    "score和sklearn.metrics下面的检测方法\n",
    "\n",
    "## 4,模型选择\n",
    "模型选择需要根据处理问题的性质来选择,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
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